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We have determined the binding strengths between ribonucleotides of adenine (A), guanine (G), uracil (U), and cytosine (C) in homogeneous single-stranded ribonucleic acids (ssRNAs) and homo-decapeptides consisting of 20 common amino acids. We use a bead-based fluorescence assay for these measurements in which decapeptides are immobilized on the bead surface and ssRNAs are in solutions. The results provide a molecular basis for analyzing selectivity, specificity, and polymorphisms of amino-acid–ribonucleotide interactions. Comparative analyses of the distribution of the binding energies reveal unique binding strength patterns assignable to each pair of amino acid and ribonucleotide originating from the chemical structures. Pronounced favorable (such as Arg–G) and unfavorable (such as Met–U) binding interactions can be identified in selected groups of amino acid and ribonucleotide pairs that could provide basis to elucidate energetics of amino-acid–ribonucleotide interactions. Such interaction selectivity, specificity, and polymorphism manifest the contributions from RNA backbone, RNA bases, as well as main chain and side chain of the amino acids. Such characteristics in peptide–RNA interactions might be helpful for understanding the mechanism of protein–RNA specific recognition and the design of RNA nano-delivery systems based on peptides and their derivatives.


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Principles of amino-acid–ribonucleotide interaction revealed by binding affinities between homogeneous oligopeptides and single-stranded RNA molecules

Show Author's information Pengyu Wang1,2,§Xiaocui Fang1,2,§Ping Li1,2Minxian Li1,2Yanlian Yang1,2( )Chen Wang1,2( )
Key Laboratory for Biological Effects of Nanomaterials and Nanosafety (Chinese Academy of Sciences (CAS)), Key Laboratory of Standardization and Measurement for Nanotechnology (CAS), CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China

§ Pengyu Wang and Xiaocui Fang contributed equally to this work.

Abstract

We have determined the binding strengths between ribonucleotides of adenine (A), guanine (G), uracil (U), and cytosine (C) in homogeneous single-stranded ribonucleic acids (ssRNAs) and homo-decapeptides consisting of 20 common amino acids. We use a bead-based fluorescence assay for these measurements in which decapeptides are immobilized on the bead surface and ssRNAs are in solutions. The results provide a molecular basis for analyzing selectivity, specificity, and polymorphisms of amino-acid–ribonucleotide interactions. Comparative analyses of the distribution of the binding energies reveal unique binding strength patterns assignable to each pair of amino acid and ribonucleotide originating from the chemical structures. Pronounced favorable (such as Arg–G) and unfavorable (such as Met–U) binding interactions can be identified in selected groups of amino acid and ribonucleotide pairs that could provide basis to elucidate energetics of amino-acid–ribonucleotide interactions. Such interaction selectivity, specificity, and polymorphism manifest the contributions from RNA backbone, RNA bases, as well as main chain and side chain of the amino acids. Such characteristics in peptide–RNA interactions might be helpful for understanding the mechanism of protein–RNA specific recognition and the design of RNA nano-delivery systems based on peptides and their derivatives.

Keywords: flow cytometry, binding affinity, peptides, single-stranded ribonucleic acid (ssRNA)

References(37)

[1]

Sharp, P. A. The centrality of RNA. Cell 2009, 136, 577–580.

[2]

Müller, F.; Escobar, L.; Xu, F.; Węgrzyn, E.; Nainytė, M.; Amatov, T.; Chan, C. Y.; Pichler, A.; Carell, T. A prebiotically plausible scenario of an RNA-peptide world. Nature 2022, 605, 279–284.

[3]

Fire, A.; Xu, S. Q.; Montgomery, M. K.; Kostas, S. A.; Driver, S. E.; Mello, C. C. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 1998, 391, 806–811.

[4]

Mendell, J. T.; Olson, E. N. MicroRNAs in stress signaling and human disease. Cell 2012, 148, 1172–1187.

[5]

He, L.; He, X. Y.; Lowe, S. W.; Hannon, G. J. MicroRNAs join the p53 network-another piece in the tumour-suppression puzzle. Nat. Rev. Cancer 2007, 7, 819–822.

[6]

Medina, P. P.; Nolde, M.; Slack, F. J. OncomiR addiction in an in vivo model of microRNA-21-induced pre-B-cell lymphoma. Nature 2010, 467, 86–90.

[7]

Huang, J. X.; Xiao, K. Nanoparticles-based strategies to improve the delivery of therapeutic small interfering RNA in precision oncology. Pharmaceutics 2022, 14, 1586.

[8]

Yang, D. C.; Eldredge, A. C.; Hickey, J. C.; Muradyan, H.; Guan, Z. Multivalent peptide-functionalized bioreducible polymers for cellular delivery of various RNAs. Biomacromolecules 2020, 21, 1613–1624.

[9]

Welch, J. J.; Swanekamp, R. J.; King, C.; Dean, D. A.; Nilsson, B. L. Functional delivery of siRNA by disulfide-constrained cyclic amphipathic peptides. ACS Med. Chem. Lett. 2016, 7, 584–589.

[10]

Kim, H.; Kitamatsu, M.; Ohtsuki, T. Combined apoptotic effects of peptide and miRNA in a peptide/miRNA nanocomplex. J. Biosci. Bioeng. 2019, 128, 110–116.

[11]

Berman, H. M.; Westbrook, J.; Feng, Z. K.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The protein data bank. Nucl. Acids Res. 2000, 28, 235–242.

[12]

Wang, X. Q.; McLachlan, J.; Zamore, P. D.; Hall, T. M. T. Modular recognition of RNA by a human pumilio-homology domain. Cell 2002, 110, 501–512.

[13]

Hentze, M. W.; Castello, A.; Schwarzl, T.; Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 2018, 19, 327–341.

[14]

Lee, Y. Y.; Kim, H.; Kim, V. N. Sequence determinant of small RNA production by DICER. Nature 2023, 615, 323–330.

[15]

Lee, Y. Y.; Lee, H.; Kim, H.; Kim, V. N.; Roh, S. H. Structure of the human DICER-pre-miRNA complex in a dicing state. Nature 2023, 615, 331–338.

[16]

Iwakawa, H. O.; Tomari, Y. Life of RISC: Formation, action, and degradation of RNA-induced silencing complex. Mol. Cell 2022, 82, 30–43.

[17]

Corley, M.; Burns, M. C.; Yeo, G. W. How RNA-binding proteins interact with RNA: Molecules and mechanisms. Mol. Cell 2020, 78, 9–29.

[18]

Messias, A. C.; Sattler, M. Structural basis of single-stranded RNA recognition. Acc. Chem. Res. 2004, 37, 279–287.

[19]

Sharma, D.; Zagore, L. L.; Brister, M. M.; Ye, X.; Crespo-Hernández, C. E.; Licatalosi, D. D.; Jankowsky, E. The kinetic landscape of an RNA-binding protein in cells. Nature 2021, 591, 152–156.

[20]

Ramanathan, M.; Porter, D. F.; Khavari, P. A. Methods to study RNA–protein interactions. Nat. Methods 2019, 16, 225–234.

[21]

Krüger, D. M.; Neubacher, S.; Grossmann, T. N. Protein–RNA interactions: Structural characteristics and hotspot amino acids. RNA 2019, 24, 1457–1465.

[22]

Jones, S.; Daley, D. T. A.; Luscombe, N. M.; Berman, H. M.; Thornton, J. M. Protein–RNA interactions: A structural analysis. Nucleic Acids Res. 2001, 29, 943–954.

[23]

Lejeune, D.; Delsaux, N.; Charloteaux, B.; Thomas, A.; Brasseur, R. Protein-nucleic acid recognition: Statistical analysis of atomic interactions and influence of DNA structure. Proteins 2005, 61, 258–271.

[24]

Auweter, S. D.; Oberstrass, F. C.; Allain, F. H. T. Sequence-specific binding of single-stranded RNA: Is there a code for recognition. Nucleic Acids Res. 2006, 34, 4943–4959.

[25]

Cléry, A.; Boudet, J.; Allain, F. H. T. Single-stranded nucleic acid recognition: Is there a code after all. Structure 2013, 21, 4–6.

[26]

Mirsky, A. E.; Pauling, L. On the structure of native, denatured, and coagulated proteins. Proc. Natl. Acad. Sci. USA 1936, 22, 439–447.

[27]

Dill, K. A. Dominant forces in protein folding. Biochemistry 1990, 29, 7133–7155.

[28]

Du, H. W.; Hu, X. Y.; Duan, H. Y.; Yu, L. L.; Qu, F. Y.; Huang, Q. X.; Zheng, W. S.; Xie, H. Y.; Peng, J. X.; Tuo, R. et al. Principles of inter-amino-acid recognition revealed by binding energies between homogeneous oligopeptides. ACS Cent. Sci. 2019, 5, 97–108.

[29]

Wang, P. Y.; Fang, X. C.; Du, R.; Wang, J. L.; Liu, M. P.; Xu, P.; Li, S. Q.; Zhang, K. Y.; Ye, S. Y.; You, Q. et al. Principles of amino-acid–nucleotide interactions revealed by binding affinities between homogeneous oligopeptides and single-stranded DNA molecules. ChemBioChem 2022, 23, e202200048.

[30]
Liu, J. S. Monte Carlo Strategies in Scientific Computing; Springer: New York, 2004.
[31]

Wang, X. Q.; Hall, T. M. T. Structural basis for recognition of AU-rich element RNA by the HuD protein. Nat. Struct. Biol. 2001, 8, 141–145.

[32]

Handa, N.; Nureki, O.; Kurimoto, K.; Kim, I.; Sakamoto, H.; Shimura, Y.; Muto, Y.; Yokoyama, S. Structural basis for recognition of the tra mRNA precursor by the sex-lethal protein. Nature 1999, 398, 579–585.

[33]

Weiss, M. A.; Narayana, N. RNA recognition by arginine-rich peptide motifs. 3.0.CO;2-8">Biopolymers 1998, 48, 167–180.

[34]

Baidya, N.; Uhlenbeck, O. C. The role of 2'-hydroxyl groups in an RNA–protein interaction. Biochemistry 1995, 34, 12363–12368.

[35]

Calnan, B. J.; Tidor, B.; Biancalana, S.; Hudson, D.; Frankel, A. D. Arginine-mediated RNA recognition: The arginine fork. Science 1991, 252, 1167–1171.

[36]

Chavali, S. S.; Cavender, C. E.; Mathews, D. H.; Wedekind, J. E. Arginine forks are a widespread motif to recognize phosphate backbones and guanine nucleobases in the RNA major groove. J. Am. Chem. Soc. 2020, 142, 19835–19839.

[37]

Levintov, L.; Vashisth, H. Role of salt–bridging interactions in recognition of viral RNA by arginine-rich peptides. Biophys. J. 2021, 120, 5060–5073.

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Publication history
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Acknowledgements

Publication history

Received: 04 May 2023
Revised: 29 June 2023
Accepted: 30 June 2023
Published: 11 August 2023
Issue date: December 2023

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 21721002, 32101130, and 31971295). Financial support from the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB36000000) is also gratefully acknowledged.

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